The new buzzword in AI circles is retrieval augmented generation, and many conversations about it involve its potential to radically improve various facets of tech businesses. That’s because RAG addresses one of the biggest weaknesses of ChatGPT, GPT-4, Bard, and other AI models.
The key issue with large language models (LLMs) is their reliance on vast yet fixed pre-training datasets. So, although they’re advanced, they fall behind when it comes to grasping new or highly specific information. This is where RAG steps in.
Retrieval augmented generation adds new dynamics to the AI model training process by incorporating an external research step. Unlike previous algorithms, RAG actively retrieves external documents or database entries in response to a query before generating an output. This allows the model to return a more informed and contextually relevant result.
That said, how will RAG impact today’s tech businesses? Let’s take a look at some real-world examples.
Harnessing Unstructured Data for Decision-Making
A vast amount of data in businesses is unstructured and hence underutilized. But imagine having AI capable of sifting through this data for insight. Fortunately, RAG has a remarkable ability to retrieve relevant unstructured, “hidden” information and generate coherent responses.
A great example of this in action is Facebook’s AI model called Dense Passage Retrieval, which uses RAG to scan billions of text passages to address complex queries. DPR has dramatically improved the efficiency of Facebook’s search function, helping it deliver more accurate information to its users and become a more effective resource for research and decision-making.
Building More Trust With AI
While AI is already incorporated in various business operations, trust is still a concern. According to a survey, 23% customers do not trust AI, while 56% remain neutral. This can change with the use of RAG.
Salesforce, a trailblazer in customer relationship management (CRM), recently added RAG capabilities to its Einstein Copilot Search, its internal search functionality. The powerful technology “reduces the hallucinations in generative AI responses, without the complexity of fine-tuning or retraining an LLM.”
But more importantly, this RAG-powered system can build more trust, as its responses can now cite the source of their data. Plus, with Salesforce’s Einstein Trust Layer, the company’s data is protected and therefore not part of the LLM’s dataset.
Enhancing Customer Service
A crucial point for any business is customer service. Consumers look for real-time, intelligent responses, and businesses often rely on older generation chatbots – their results can, at times, be less than satisfactory. RAG can redefine this interaction.
Take a look at Gradient AI, a startup that specializes in AI solutions for insurance companies. They’ve now leveraged chatbots equipped with RAG and Gradient Embeddings to facilitate their customers’ claim process.
These features allow the bots to retrieve accurate information for the huge data of insurance policies, claims, and regulations. With this, they can generate precise answers to customer inquiries and subsequently decrease claims processing time.
How Can Businesses Implement RAG-Based Solutions?
Understanding the value and potential RAG brings into the AI ecosystem is only a part of the equation; it’s even more crucial for businesses to know how to integrate it effectively. Here’s a suggested roadmap:
1. Identify specific business challenges: Determine the key areas of your business where information retrieval and insightful data integration can add the most value. These could include data-heavy departments like sales and customer service, or even strategic product development areas.
2. Choosing the right partner: Implementing RAG solutions needs specific expertise. Businesses can either build their in-house teams or collaborate with AI solution providers. Companies like OpenAI and MongoDB are constantly developing and sharing resources on systems like RAG, while Microsoft has its formatted dataset designed to develop and evaluate models.
3. Pilot before scaling: Test the waters by piloting RAG solutions in specific areas. For instance, a RAG-powered chatbot can first be introduced in a single department. Use the insights from this pilot to fine-tune your strategies and gradually scale up the implementation across the enterprise.
4. Continual learning and adaptation: The AI landscape is an ever-evolving one, and continuous learning is key to staying ahead. Invest in regular training and upskilling your teams so they can take full advantage of RAG systems and subsequent improvements in the technology.
Companies like Google and OpenAI have demonstrated that RAG can have transformative impacts on tech businesses. But it’s not just tech giants that can benefit from RAG. Small and medium enterprises can leverage this technology to improve their operational efficiency and customer experience while gaining an edge in the market.
